# coding=utf-8
# Copyright 2024 The TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

r"""Generate dsprites like files, smaller and with random data.

"""

import os

from absl import app
from absl import flags
import h5py
import numpy as np
from tensorflow_datasets.core import utils
from tensorflow_datasets.testing import test_utils

NUM_IMAGES = 5
FACTOR_COUNTS = [1, 3, 6, 40, 32, 32]
FACTOR_VALUES = [
    [1.0],
    [1.0, 2.0, 3.0],
    [0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
    [
        0.0,
        0.16110732,
        0.32221463,
        0.48332195,
        0.64442926,
        0.80553658,
        0.96664389,
        1.12775121,
        1.28885852,
        1.44996584,
        1.61107316,
        1.77218047,
        1.93328779,
        2.0943951,
        2.25550242,
        2.41660973,
        2.57771705,
        2.73882436,
        2.89993168,
        3.061039,
        3.22214631,
        3.38325363,
        3.54436094,
        3.70546826,
        3.86657557,
        4.02768289,
        4.1887902,
        4.34989752,
        4.51100484,
        4.67211215,
        4.83321947,
        4.99432678,
        5.1554341,
        5.31654141,
        5.47764873,
        5.63875604,
        5.79986336,
        5.96097068,
        6.12207799,
        6.28318531,
    ],
    [
        0.0,
        0.03225806,
        0.06451613,
        0.09677419,
        0.12903226,
        0.16129032,
        0.19354839,
        0.22580645,
        0.25806452,
        0.29032258,
        0.32258065,
        0.35483871,
        0.38709677,
        0.41935484,
        0.4516129,
        0.48387097,
        0.51612903,
        0.5483871,
        0.58064516,
        0.61290323,
        0.64516129,
        0.67741935,
        0.70967742,
        0.74193548,
        0.77419355,
        0.80645161,
        0.83870968,
        0.87096774,
        0.90322581,
        0.93548387,
        0.96774194,
        1.0,
    ],
    [
        0.0,
        0.03225806,
        0.06451613,
        0.09677419,
        0.12903226,
        0.16129032,
        0.19354839,
        0.22580645,
        0.25806452,
        0.29032258,
        0.32258065,
        0.35483871,
        0.38709677,
        0.41935484,
        0.4516129,
        0.48387097,
        0.51612903,
        0.5483871,
        0.58064516,
        0.61290323,
        0.64516129,
        0.67741935,
        0.70967742,
        0.74193548,
        0.77419355,
        0.80645161,
        0.83870968,
        0.87096774,
        0.90322581,
        0.93548387,
        0.96774194,
        1.0,
    ],
]
OUTPUT_NAME = "dsprites_ndarray_co1sh3sc6or40x32y32_64x64.hdf5"

flags.DEFINE_string(
    "tfds_dir",
    os.fspath(utils.tfds_write_path()),
    "Path to tensorflow_datasets directory",
)
FLAGS = flags.FLAGS


def _create_fake_samples():
  """Creates a fake set of samples.

  Returns:
    Tuple with fake images, class labels and latent values.
  """
  rs = np.random.RandomState(0)
  images = rs.randint(256, size=(NUM_IMAGES, 64, 64)).astype("uint8")

  classes = []
  values = []
  for num_factors, factor_values in zip(FACTOR_COUNTS, FACTOR_VALUES):
    classes.append(rs.randint(num_factors, size=(NUM_IMAGES), dtype=np.int64))
    values.append(rs.choice(factor_values, size=(NUM_IMAGES)))

  return images, classes.T, values.T  # pytype: disable=attribute-error


def _generate():
  """Generates a fake data set and writes it to the fake_examples directory."""
  output_dir = os.path.join(
      FLAGS.tfds_dir, "testing", "test_data", "fake_examples", "dsprites"
  )
  test_utils.remake_dir(output_dir)

  images, classes, values = _create_fake_samples()

  with h5py.File(os.path.join(output_dir, OUTPUT_NAME), "w") as f:
    img_dataset = f.create_dataset("imgs", images.shape, "|u1")
    img_dataset.write_direct(images)

    classes_dataset = f.create_dataset("latents/classes", classes.shape, "<i8")
    classes_dataset.write_direct(np.ascontiguousarray(classes))

    values_dataset = f.create_dataset("latents/values", values.shape, "<f8")
    values_dataset.write_direct(np.ascontiguousarray(values))


def main(argv):
  if len(argv) > 1:
    raise app.UsageError("Too many command-line arguments.")
  _generate()


if __name__ == "__main__":
  app.run(main)
